{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 0
   },
   "source": [
    "# Neural Network Inference\n",
    ":label:`ch_from_mxnet`\n",
    "\n",
    "You have seen how to implement and compile a simple vector addition operator in :numref:`ch_vector_add`. Now we will make a big jump to compile a whole pre-trained neural network, which consists of a set of operators, to run the inference.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "1"
    },
    "origin_pos": 1,
    "tab": [
     "tvm"
    ]
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import mxnet as mx\n",
    "from PIL import Image\n",
    "import tvm\n",
    "from tvm import relay"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 2
   },
   "source": [
    "Here three additional modules are imported compared to the previous chapter. We will use `PIL` to read images, `mxnet` to obtain pre-trained neural networks, and the `relay` module :cite:`Roesch.Lyubomirsky.Kirisame.ea.2019` in TVM to convert and optimize a neural network.\n",
    "`Relay` is the high-level intermediate representation (IR) in TVM to represent a neural network.\n",
    "\n",
    "## Obtaining Pre-trained Models\n",
    "\n",
    "A pre-trained model means a neural network with parameters trained on a data set. Here we download and load a ResNet-18 model by specifying `pretrained=True` from MXNet's model zoo :cite:`Chen.Li.Li.ea.2015`. If you want to know details about this model, please refer to [Chapter 7.6 in D2L](http://d2l.ai/chapter_convolutional-modern/resnet.html). You can find more models on the [MXNet model zoo](https://mxnet.apache.org/api/python/docs/api/gluon/model_zoo/index.html) page, or refer to [GluonCV](https://gluon-cv.mxnet.io/model_zoo/index.html) and [GluonNLP](http://gluon-nlp.mxnet.io/model_zoo/index.html) for more computer vision and natural language models.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "2"
    },
    "origin_pos": 3,
    "tab": [
     "tvm"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13, Dense(512 -> 1000, linear))"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = mx.gluon.model_zoo.vision.resnet18_v2(pretrained=True)\n",
    "len(model.features), model.output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 4
   },
   "source": [
    "The loaded model is trained on the Imagenet 1K dataset, which contains around 1 million natural object images among 1000 classes. The model has two parts, the main body part `model.features` contains 13 blocks, and the output layer is a dense layer with 1000 outputs.\n",
    "\n",
    "The following code block loads the text labels for each class in the Imagenet dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "3"
    },
    "origin_pos": 5,
    "tab": [
     "tvm"
    ]
   },
   "outputs": [],
   "source": [
    "with open('../data/imagenet1k_labels.txt') as f:\n",
    "    labels = eval(f.read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 6
   },
   "source": [
    "## Pre-processing Data\n",
    "\n",
    "We first read a sample image. It is resized to the size, i.e. 224 px width and height, which we used to train the neural network.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "4"
    },
    "origin_pos": 7,
    "tab": [
     "tvm"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=224x224 at 0x7F7035FE0668>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image = Image.open('../data/cat.jpg').resize((224, 224))\n",
    "image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 8
   },
   "source": [
    "According to the [model zoo page](https://mxnet.apache.org/api/python/docs/api/gluon/model_zoo/index.html). Image pixels are normalized on each color channel, and the data layout is `(batch, RGB channels, height, width)`. The following function transforms the input image to satisfy the requirement.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "5"
    },
    "origin_pos": 9,
    "tab": [
     "tvm"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 3, 224, 224)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save to the d2ltvm package\n",
    "def image_preprocessing(image):\n",
    "    image = np.array(image) - np.array([123., 117., 104.])\n",
    "    image /= np.array([58.395, 57.12, 57.375])\n",
    "    image = image.transpose((2, 0, 1))\n",
    "    image = image[np.newaxis, :]\n",
    "    return image.astype('float32')\n",
    "\n",
    "x = image_preprocessing(image)\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 10
   },
   "source": [
    "## Compile Pre-trained Models\n",
    "\n",
    "To compile a model, we first express the MXNet model in Relay IR, which the `from_mxnet` method could help.\n",
    "In the method, we provide the model with the input data shape. Some neural networks may require some dimension(s) of the data shape to be determined later.\n",
    "However, in ResNet model the data shape is fixed, which makes it easier for the compiler to achieve high performance.\n",
    "We will mostly stick to fixed data shape in the book. We only touch the dynamic data shape (i.e. some dimension(s) to be determined in runtime) in very late chapters.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "6"
    },
    "origin_pos": 11,
    "tab": [
     "tvm"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tvm.ir.module.IRModule, dict)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "relay_mod, relay_params = relay.frontend.from_mxnet(model, {'data': x.shape})\n",
    "type(relay_mod), type(relay_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 12
   },
   "source": [
    "This method will return the program `relay_mod`, which is a `relay` module, and a dictionary of parameters `relay_params` that maps a string key to a TVM ndarray. Next, we lower the module to some lower-level IR which can be consumed by `llvm` backend. [LLVM](https://en.wikipedia.org/wiki/LLVM) defines an IR that has been adopted by multiple programming languages. The LLVM compiler is then able to compile the generated programs into machine codes for CPUs. We have already used it to compile the vector addition operator in :numref:`ch_vector_add`, despite that we didn't explicitly specify it.\n",
    "\n",
    "In addition, we set the optimization level to the highest level 3. You may get warning messages that not every operator is well optimized, you can ignore it for now. We will get back to it later.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "7"
    },
    "origin_pos": 13,
    "tab": [
     "tvm"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cannot find config for target=llvm, workload=('dense_nopack.x86', ('TENSOR', (1, 512), 'float32'), ('TENSOR', (1000, 512), 'float32'), None, 'float32'). A fallback configuration is used, which may bring great performance regression.\n"
     ]
    }
   ],
   "source": [
    "target = 'llvm'\n",
    "with relay.build_config(opt_level=3):\n",
    "    graph, mod, params = relay.build(relay_mod, target, params=relay_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 14
   },
   "source": [
    "The compiled module has three parts: `graph` is a json string described the neural network, `mod` is a library that contains all compiled operators used to run the inference, and `params` is a dictionary mapping parameter name to weights.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "8"
    },
    "origin_pos": 15,
    "tab": [
     "tvm"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(str, tvm.runtime.module.Module, dict)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(graph), type(mod), type(params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 16
   },
   "source": [
    "You can view `mod` as a TVM module we already seen in :numref:`ch_vector_add`.\n",
    "\n",
    "## Inference\n",
    "\n",
    "Now we can create a runtime to run the model inference, namely the forward pass of a neural network. Creating the runtime needs the neural network definition in json (i.e. `graph`) and the library that contains machine code of compiled operators (i.e. `mod`), with a device context that can be constructed from the target. The device is CPU here, specified by `llvm`. Next we load the parameters with `set_input` and run the workload by feeding the input data. Since this network has a single output layer, we can obtain it, a `(1, 1000)` shape matrix, by `get_output(0)`. The final output is a 1000-length NumPy vector.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "9"
    },
    "origin_pos": 17,
    "tab": [
     "tvm"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000,)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctx = tvm.context(target)\n",
    "rt = tvm.contrib.graph_runtime.create(graph, mod, ctx)\n",
    "rt.set_input(**params)\n",
    "rt.run(data=tvm.nd.array(x))\n",
    "scores = rt.get_output(0).asnumpy()[0]\n",
    "scores.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 18
   },
   "source": [
    "The vector contains the predicted confidence score for each class. Note that the pre-trained model doesn't have the [softmax](https://en.wikipedia.org/wiki/Softmax_function) operator, so these scores are not mapped into probabilities in (0, 1). Now we can find the two largest scores and report their labels.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "10"
    },
    "origin_pos": 19,
    "tab": [
     "tvm"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('tiger cat', 'Egyptian cat')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.argsort(scores)[-1:-5:-1]\n",
    "labels[a[0]], labels[a[1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 20
   },
   "source": [
    "## Saving the Compiled Library\n",
    "\n",
    "We can save the output of `relay.build` in disk to reuse them later. The following code block saves the json string, library, and parameters.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "11"
    },
    "origin_pos": 21,
    "tab": [
     "tvm"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-r--r-- 1 jenkins jenkins  45M Oct 13 10:45 resnet18.params\r\n",
      "-rw-r--r-- 1 jenkins jenkins  28K Oct 13 10:45 resnet18.json\r\n",
      "-rw-r--r-- 1 jenkins jenkins 157K Oct 13 10:45 resnet18.tar\r\n"
     ]
    }
   ],
   "source": [
    "!rm -rf resnet18*\n",
    "\n",
    "name = 'resnet18'\n",
    "graph_fn, mod_fn, params_fn = [name+ext for ext in ('.json','.tar','.params')]\n",
    "mod.export_library(mod_fn)\n",
    "with open(graph_fn, 'w') as f:\n",
    "    f.write(graph)\n",
    "with open(params_fn, 'wb') as f:\n",
    "    f.write(relay.save_param_dict(params))\n",
    "\n",
    "!ls -alht resnet18*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 22
   },
   "source": [
    "Now we load the saved module back.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "12"
    },
    "origin_pos": 23,
    "tab": [
     "tvm"
    ]
   },
   "outputs": [],
   "source": [
    "loaded_graph = open(graph_fn).read()\n",
    "loaded_mod = tvm.runtime.load_module(mod_fn)\n",
    "loaded_params = open(params_fn, \"rb\").read()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 24
   },
   "source": [
    "And then construct the runtime as before to verify the results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "13"
    },
    "origin_pos": 25,
    "tab": [
     "tvm"
    ]
   },
   "outputs": [],
   "source": [
    "loaded_rt = tvm.contrib.graph_runtime.create(loaded_graph, loaded_mod, ctx)\n",
    "loaded_rt.load_params(loaded_params)\n",
    "loaded_rt.run(data=tvm.nd.array(x))\n",
    "loaded_scores = loaded_rt.get_output(0).asnumpy()[0]\n",
    "tvm.testing.assert_allclose(loaded_scores, scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 26
   },
   "source": [
    "## Summary\n",
    "\n",
    "- We can use `relay` of TVM to convert and compile a neural network into a module for model inference.\n",
    "- We can save the compiled module into disk to facilitate future deployment.\n",
    "\n",
    "## [Discussions](https://discuss.tvm.ai/t/getting-started-neural-network-inference/4708)\n"
   ]
  }
 ],
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